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Phase unwrapping is a key problem in many coherent imaging systems, such assynthetic aperture radar (SAR) interferometry. A general formulation forredundant integration of finite differences for phase unwrapping (Costantini etal., 2010) was shown to produce a more reliable solution by exploitingredundant differential estimates. However, this technique requires a commerciallinear programming solver for large-scale problems. For a linear cost function,we propose a method based on Dual Decomposition that breaks the given problemdefined over a non-planar graph into tractable sub-problems over planarsubgraphs. We also propose a decomposition technique that exploits theunderlying graph structure for solving the sub-problems efficiently andguarantees asymptotic convergence to the globally optimal solution. Theexperimental results demonstrate that the proposed approach is comparable tothe existing state-of-the-art methods in terms of the estimate with a betterruntime and memory footprint.

PURE: Scalable Phase Unwrapping with Spatial Redundant Arcs